Nerd Watching: GPU-Powered AI Helps Researchers Identify Individual Birds

Anyone can tell an eagle from an ostrich. It takes a skilled birdwatcher to tell a chipping sparrow from a house sparrow from an American tree sparrow.

Now researchers are using AI to take this to the next level — identifying individual birds.

André Ferreira, a Ph.D. student at France’s Centre for Functional and Evolutionary Ecology, harnessed an NVIDIA GeForce RTX 2070 to train a powerful AI that identifies individual birds within the same species.

It’s the latest example of how deep learning has become a powerful tool for wildlife biologists studying a wide range of animals.

Marine biologists with the U.S. National Oceanic and Atmospheric Research Organization use deep learning to identify and track the endangered North Atlantic Right Whale. Zoologist Dan Rubenstein uses deep learning to distinguish between individuals in herds of Grevy’s Zebras.

The sociable weaver isn’t endangered. But understanding the role of an individual in a group is key to understanding how the birds, native to Southern Africa, work together to build their nests.

The problem: it’s hard to tell the small, rust-colored birds apart, especially when trying to capture their activities in the wild.

In a paper released last week, Ferreira detailed how he and a team of researchers trained a convolutional neural network to identify individual birds.

Ferreira built his model using Keras, a popular open-source neural network library, running on a GeForce RTX 2070 GPU.

He then teamed up with researchers at Germany’s Max Planck Institute of Animal Behavior. Together, they adapted the model to identify wild great tits and captive zebra finches, two other widely studied bird species.

To train their models — a crucial step towards building any modern deep-learning-based AI — researchers made feeders equipped with cameras.

The researchers fitted birds with electronic tags, which triggered sensors in the feeders alerting researchers to the bird’s identity.

This data gave the model a “ground truth” that it could check against for accuracy.

The team’s AI was able to identify individual sociable weavers and wild great tits more than 90 percent of the time. And it identified captive zebra finches 87 percent of the time.

For bird researchers, the work promises several key benefits.

Using cameras and other sensors to track birds allows researchers to study bird behavior much less invasively.

With less need to put researchers in the field, the technique allows researchers to track bird behavior over more extended periods.

Next: Ferreira and his colleagues are working to build AI that can recognize individual birds it has never seen before, and better track groups of birds.

Birdwatching may never be the same.

Featured image credit: Bernard DuPont, some rights reserved.

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